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Investigating Contextual Influence in Document-Level Translation

Nayak, Prashanth, Haque, Rejwanul, Kelleher, John D. and Way, Andy (2022) Investigating Contextual Influence in Document-Level Translation. Information 2022, 13 (5). pp. 1-14. ISSN 2078-2489

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Current state-of-the-art neural machine translation (NMT) architectures usually do not take document-level context into account. However, the document-level context of a source sentence to be translated could encode valuable information to guide the MT model to generate a better translation. In recent times, MT researchers have turned their focus to this line of MT research. As an example, hierarchical attention network (HAN) models use document-level context for translation prediction. In this work, we studied translations produced by the HAN-based MT systems. We examined how contextual information improves translation in document-level NMT. More specifically, we investigated why context-aware models such as HAN perform better than vanilla baseline NMT systems that do not take context into account. We considered Hindi-to-English, Spanish-to-English and Chinese-to-English for our investigation. We experimented with the formation of conditional context (i.e., neighbouring sentences) of the source sentences to be translated in HAN to predict their target translations. Interestingly, we observed that the quality of the target translations of specific source sentences highly relates to the context in which the source sentences appear. Based on their sensitivity to context, we classify our test set sentences into three categories, i.e., context-sensitive, context-insensitive and normal. We believe that this categorization may change the way in which context is utilized in document-level translation.

Item Type: Article
Uncontrolled Keywords: Machine translation; neural machine translation; context-aware translation; document translation
Subjects: Q Science > QA Mathematics > Electronic computers. Computer science
T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science
P Language and Literature > P Philology. Linguistics > Language Services
Divisions: School of Computing > Staff Research and Publications
Depositing User: Clara Chan
Date Deposited: 27 Sep 2022 10:08
Last Modified: 27 Sep 2022 10:08

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